The overarching goal of Core A is to provide the basic Adnninistrative, Data Centralization and Management, and Statistical Services/Data Integration required by all projects to: (i) Provide unified administrative services; (ii) Facilitate scientific and administrative communication; (iii) Manage the central data archives and facilitate exchange of verified data; (iv) Coordinate statistical services consultation for the participating investigators; and (v) Apply bioinformatics / neurocomputational approaches to integrate our multimodal data, from the molecular genetic level to that of social behavior. To this end, the Objectives are: (a) Administrative Services Fostering Interactivity and Integration, to coordinate daily functioning of a complex interdisciplinary research enterprise by coordinating communications and scientific activity among the Projects and Cores, providing budgetary planning and control, and facilitating and promoting collaboration and interactivity among investigators and consultants; (b) Data Centralization and Management, Data Quality Control, Core Statistical Services, and Multidimensional Data Integration, to maintain a secure, centralized, and immediately sharable database in order to facilitate collaboration and integration, to provide basic statistical consulting services, and to coordinate data integration by applying bioinformatics / neurocomputational approaches to our large database to reveal cross-level associations. The vitally important functions of Core A thus help to manage and integrate data from different domains with the ultimate goal of characterizing the system of human social behavior against the backdrop ofthe WS social phenotype. The testimony of the enormous success of Core A function as an Administrative and Data Centralization and Integration Core is our highly impressive list of interdisciplinary publications stemming from the decade of this Program Project. RELEVANCE (See instructions): Our goal is to integrate the gene, neural systems and behavioral project findings to forward our understanding of Williams syndrome. This study may help identify educational, social and medical-health support approaches appropriate for WS. The new bioinformatics and computer modeling techniques developed for this study will also be applicable to other multi-level brain research studies.